OpenRaiser/Intern-Atlas huggingface dataset:
Episodic Exploration for Deep Deterministic Policies: An Application to StarCraft Micromanagement Tasks
Venue: arXiv cs.AI 2016
Year: 2016

We consider scenarios from the real-time strategy game StarCraft as new benchmarks for reinforcement learning algorithms. We propose micromanagement tasks, which present the problem of the short-term, low-level control of army members during a battle. From a reinforcement learning point of view, these scenarios are challenging because the state-action space is very large, and because there is no obvious feature representation for the state-action evaluation function. We describe our approach to tackle the micromanagement scenarios with deep neural network controllers from raw state features given by the game engine. In addition, we present a heuristic reinforcement learning algorithm which combines direct exploration in the policy space and backpropagation. This algorithm allows for the collection of traces for learning using deterministic policies, which appears much more efficient than, for example, ε-greedy exploration. Experiments show that with this algorithm, we successfully learn non-trivial strategies for scenarios with armies of up to 15 agents, where both Q-learning and REINFORCE struggle.
Message Authentication and Secret Key Agreement in VANETs via Angle of Arrival
Venue: arXiv cs.CR 2016
Year: 2016

In the scope of VANETs, nature of exchanged safety/warning messages renders itself highly location dependent as it is usually for incident reporting. Thus, vehicles are required to periodically exchange beacon messages that include speed, time and GPS location information. In this paper paper, we present a physical layer assisted message authentication scheme that uses Angle of Arrival (AoA) estimation to verify the message originator location based on the claimed location information. Within the considered vehicular communication settings, fundamental limits of AoA estimation are developed in terms of its Cramer Rao Bound (CRB) and existence of efficient estimator. The problem of deciding whether the received signal is originated from the claimed GPS location is formulated as a two sided hypotheses testing problem whose solution is given by Wald test statics. Moreover, we use correct decision, $P_D$, and false alarm, $P_F$, probabilities as a quantitative performance measure. The observation posterior likelihood function is shown to satisfy regularity conditions necessary for asymptotic normality of the ML-AoA estimator. Thus, we give $P_D$ and $P_F$ in a closed form. We extend the potential of physical layer contribution in security to provide physical layer assisted secret key agreement (SKA) protocol. A public key (PK) based SKA in which communicating vehicles are required to validate their respective physical location. We show that the risk of the Man in the Middle attack, which is common in PK-SKA protocols without a trusted third party, is waived up to the literal meaning of the word "middle".
A Short Mechanized Proof of the Church-Rosser Theorem by the Z-property for the $λβ$-calculus in Nominal Isabelle
Venue: arXiv cs.LO 2016
Year: 2016

We present a short proof of the Church-Rosser property for the lambda-calculus enjoying two distinguishing features: Firstly, it employs the Z-property, resulting in a short and elegant proof; and secondly, it is formalized in the nominal higher-order logic available for the proof assistant Isabelle/HOL.
Honey: A dataflow programming language for the processing, featurization and analysis of multivariate, asynchronous and non-uniformly sampled scalar symbolic time sequences
Venue: arXiv cs.PL 2016
Year: 2016

We introduce HONEY; a new specialized programming language designed to facilitate the processing of multivariate, asynchronous and non-uniformly sampled symbolic and scalar time sequences. When compiled, a Honey program is transformed into a static process flow diagram, which is then executed by a virtual machine. Honey's most notable features are: (1) Honey introduces a new, efficient and non-prone to error paradigm for defining recursive process flow diagrams from text input with the mindset of imperative programming. Honey's specialized, high level and concise syntax allows fast and easy writing, reading and maintenance of complex processing of large scalar symbolic time sequence datasets. (2) Honey guarantees programs will be executed similarly on static or real-time streaming datasets. (3) Honey's IDE includes an interactive visualization tool which allows for an interactive exploration of the intermediate and final outputs. This combination enables fast incremental prototyping, debugging, monitoring and maintenance of complex programs. (4) In case of large datasets (larger than the available memory), Honey programs can be executed to process input greedily. (5) The graphical structure of a compiled program provides several desirable properties, including distributed and/or paralleled execution, memory optimization, and program structure visualization. (6) Honey contains a large library of both common and novel operators developed through various research projects. An open source C++ implementation of Honey as well as the Honey IDE and the interactive data visualizer are publicly available.
Network Inference from Grouped Data
Venue: arXiv cs.SI 2016
Year: 2016

In medical research, economics, and the social sciences data frequently appear as subsets of a set of objects. Over the past century a number of descriptive statistics have been developed to construct network structure from such data. However, these measures lack a generating mechanism that links the inferred network structure to the observed groups. To address this issue, we propose a model-based approach called the Hub Model which assumes that every observed group has a leader and that the leader has brought together the other members of the group. The performance of Hub Models is demonstrated by simulation studies. We apply this model to infer the relationships among Senators serving in the 110th United States Congress, the characters in a famous 18th century Chinese novel, and the distribution of flora in North America.
Joint Power and Subcarrier Allocation for Multicarrier Full-Duplex Systems
Venue: arXiv cs.IT 2016
Year: 2016

In this paper, we investigate resource allocation for multicarrier communication systems employing a full-duplex base station for serving multiple half-duplex downlink and uplink users simultaneously. We study the joint power and subcarrier allocation design for the maximization of the weighted sum throughput of the system. The algorithm design is formulated as a mixed combinatorial non-convex optimization problem and obtaining the globally optimal solution may require prohibitively high computational complexity. Therefore, a low computational complexity suboptimal iterative algorithm exploiting successive convex approximation is proposed to obtain a locally optimal solution. Simulation results confirm that the proposed suboptimal algorithm obtains a substantial improvement in system throughput compared to various existing baseline schemes.
Recovering the History of Informed Consent for Data Science and Internet Industry Research Ethics
Venue: arXiv cs.CY 2016
Year: 2016

Respect for persons is a cornerstone value for any conception of research ethics--though how to best realize respect in practice is an ongoing question. In the late 19th and early 20th centuries, "informed consent" emerged as a particular way to operationalize respect in medical and behavioral research contexts. Today, informed consent has been challenged by increasingly advanced networked information and communication technologies (ICTs) and the massive amounts of data they produce--challenges that have led many researchers and private companies to abandon informed consent as untenable or infeasible online. Against any easy dismissal, we aim to recover insights from the history of informed consent as it developed from the late 19th century to today. With a particular focus on the United States policy context, we show how informed consent is not a fixed or monolithic concept that should be abandoned in view of new data-intensive and technological practices, but rather it is a mechanism that has always been fluid--it has constantly evolved alongside the specific contexts and practices it is intended to regulate. Building on this insight, we articulate some specific challenges and lessons from the history of informed consent that stand to benefit current discussions of informed consent and research ethics in the context of data science and Internet industry research.
A short review and primer on respiration in human computer interaction applications
Venue: arXiv cs.HC 2016
Year: 2016

The application of psychophysiology in human-computer interaction is a growing field with significant potential for future smart personalised systems. Working in this emerging field requires comprehension of an array of physiological signals and analysis techniques. Respiration is unique among physiological signals in that it can be consciously controlled which has to be taken into account when designing applications. Respiration is tightly connected to other physiological signals, especially cardiovascular activity, and often analyzed in conjunction with other signals. When analyzed separately, an increase in the rate of respiration can be seen as an increase in metabolic demand which indicates activate states such as engagement and attention. We present a short review on the application of respiration in human-computer interaction. This paper aims to serve as a primer for the novice, enabling rapid familiarisation with the latest core concepts. We put special emphasis on everyday human-computer interface applications to distinguish from the more common clinical or sports uses of psychophysiology. This paper is an extract from a comprehensive review of the entire field of ambulatory psychophysiology, including 12 similar chapters, plus application guidelines and systematic review. Thus any citation should be made using the following reference: B. Cowley, M. Filetti, K. Lukander, J. Torniainen, A. Henelius, L. Ahonen, O. Barral, I. Kosunen, T. Valtonen, M. Huotilainen, N. Ravaja, G. Jacucci. The Psychophysiology Primer: a guide to methods and a broad review with a focus on human-computer interaction. Foundations and Trends in Human-Computer Interaction, vol. 9, no. 3-4, pp. 150--307, 2016.
Formalized Confluence of Quasi-Decreasing, Strongly Deterministic Conditional TRSs
Venue: arXiv cs.LO 2016
Year: 2016

We present an Isabelle/HOL formalization of a characterization of confluence for quasi-reductive strongly deterministic conditional term rewrite systems, due to Avenhaus and Loría-Sáenz.
Low-Rank Tensor Decomposition-Aided Channel Estimation for Millimeter Wave MIMO-OFDM Systems
Venue: arXiv cs.IT 2016
Year: 2016

We consider the problem of downlink channel estimation for millimeter wave (mmWave) MIMO-OFDM systems, where both the base station (BS) and the mobile station (MS) employ large antenna arrays for directional precoding/beamforming. Hybrid analog and digital beamforming structures are employed in order to offer a compromise between hardware complexity and system performance. Different from most existing studies that are concerned with narrowband channels, we consider estimation of wideband mmWave channels with frequency selectivity, which is more appropriate for mmWave MIMO-OFDM systems. By exploiting the sparse scattering nature of mmWave channels, we propose a CANDECOMP/PARAFAC (CP) decomposition-based method for channel parameter estimation (including angles of arrival/departure, time delays, and fading coefficients). In our proposed method, the received signal at the BS is expressed as a third-order tensor. We show that the tensor has the form of a low-rank CP decomposition, and the channel parameters can be estimated from the associated factor matrices. Our analysis reveals that the uniqueness of the CP decomposition can be guaranteed even when the size of the tensor is small. Hence the proposed method has the potential to achieve substantial training overhead reduction. We also develop Cramer-Rao bound (CRB) results for channel parameters, and compare our proposed method with a compressed sensing-based method. Simulation results show that the proposed method attains mean square errors that are very close to their associated CRBs, and presents a clear advantage over the compressed sensing-based method in terms of both estimation accuracy and computational complexity.
Modelling Creativity: Identifying Key Components through a Corpus-Based Approach
Venue: arXiv cs.CL 2016
Year: 2016

Creativity is a complex, multi-faceted concept encompassing a variety of related aspects, abilities, properties and behaviours. If we wish to study creativity scientifically, then a tractable and well-articulated model of creativity is required. Such a model would be of great value to researchers investigating the nature of creativity and in particular, those concerned with the evaluation of creative practice. This paper describes a unique approach to developing a suitable model of how creative behaviour emerges that is based on the words people use to describe the concept. Using techniques from the field of statistical natural language processing, we identify a collection of fourteen key components of creativity through an analysis of a corpus of academic papers on the topic. Words are identified which appear significantly often in connection with discussions of the concept. Using a measure of lexical similarity to help cluster these words, a number of distinct themes emerge, which collectively contribute to a comprehensive and multi-perspective model of creativity. The components provide an ontology of creativity: a set of building blocks which can be used to model creative practice in a variety of domains. The components have been employed in two case studies to evaluate the creativity of computational systems and have proven useful in articulating achievements of this work and directions for further research.
Nonvolatile Multi-level Memory and Boolean Logic Gates Based on a Single Memtranstor
Venue: arXiv cs.ET 2016
Year: 2016

Memtranstor that correlates charge and magnetic flux via nonlinear magnetoelectric effects has a great potential in developing next-generation nonvolatile devices. In addition to multi-level nonvolatile memory, we demonstrate here that nonvolatile logic gates such as NOR and NAND can be implemented in a single memtranstor made of the Ni/PMN-PT/Ni heterostructure. After applying two sequent voltage pulses (X1, X2) as the logic inputs on the memtranstor, the output magnetoelectric voltage can be positive high (logic "1"), positive low (logic "0"), or negative (logic "0"), depending on the levels of X1 and X2. The underlying physical mechanism is related to the complete or partial reversal of ferroelectric polarization controlled by inputting selective voltage pulses, which determines the magnitude and sign of the magnetoelectric voltage coefficient. The combined functions of both memory and logic could enable the memtranstor as a promising candidate for future computing systems beyond von Neumann architecture.
A Multi-Scale Cascade Fully Convolutional Network Face Detector
Venue: arXiv cs.CV 2016
Year: 2016

Face detection is challenging as faces in images could be present at arbitrary locations and in different scales. We propose a three-stage cascade structure based on fully convolutional neural networks (FCNs). It first proposes the approximate locations where the faces may be, then aims to find the accurate location by zooming on to the faces. Each level of the FCN cascade is a multi-scale fully-convolutional network, which generates scores at different locations and in different scales. A score map is generated after each FCN stage. Probable regions of face are selected and fed to the next stage. The number of proposals is decreased after each level, and the areas of regions are decreased to more precisely fit the face. Compared to passing proposals directly between stages, passing probable regions can decrease the number of proposals and reduce the cases where first stage doesn't propose good bounding boxes. We show that by using FCN and score map, the FCN cascade face detector can achieve strong performance on public datasets.
Wireless Security with Beamforming Technique
Venue: arXiv cs.IT 2016
Year: 2016

This thesis focuses on the wireless security in the physical layer with beamforming technique. One of the emerging areas is the security enhancement in the physical layer, which exploits the intrinsic properties of the wireless medium. Beamforming, which has been proved to have many advantages, can also be applied to enhance the wireless security. One of the most common threats, i.e., passive eavesdropping, is addressed in this thesis. To reduce the risk of leaking information to the eavesdroppers, the physical region where the transmission is exposed to eavesdropping has been studied. In this thesis, the exposure region based beamforming technique is proposed to combat the threat from the randomly located eavesdroppers in a fading channel. In this system model, the large-scale path loss and a general Rician fading channel model are considered. The exposure region is established to describe the secrecy performance of the system, based on which the probability that the secrecy outage event occurs is defined to evaluate the security level of the exposure region. The antenna array is one of the most important factors that affect the secrecy performance of the exposure region based beamforming technique. The potential of using different array geometry and array configuration to improve security is explored. In this thesis, two common arrays, i.e., linear and circular arrays, are considered.Analytic expressions for general array geometry and for the linear and circular arrays are derived. Based on the empirical results, numerical optimization algorithms are developed to exploit the array configuration to enhance the system security level. In addition, experiments are carried out to study the performance of the beamformer with linear and circular arrays. Especially, the impact of the mutual coupling on the security performance is investigated.
Kruskal's Tree Theorem for Acyclic Term Graphs
Venue: arXiv cs.LO 2016
Year: 2016

In this paper we study termination of term graph rewriting, where we restrict our attention to acyclic term graphs. Motivated by earlier work by Plump we aim at a definition of the notion of simplification order for acyclic term graphs. For this we adapt the homeomorphic embedding relation to term graphs. In contrast to earlier extensions, our notion is inspired by morphisms. Based on this, we establish a variant of Kruskal's Tree Theorem formulated for acyclic term graphs. In proof, we rely on the new notion of embedding and follow Nash-Williams' minimal bad sequence argument. Finally, we propose a variant of the lexicographic path order for acyclic term graphs.
Large-scale multiscale particle models in inhomogeneous domains: modelling and implementation
Venue: arXiv cs.CE 2016
Year: 2016

In this thesis, we develop multiscale models for particle simulations in population dynamics. These models are characterised by prescribing particle motion on two spatial scales: microscopic and macroscopic. At the microscopic level, each particle has its own mass, position and velocity, while at the macroscopic level the particles are interpolated to a continuum quantity whose evolution is governed by a system of transport equations. This way, one can prescribe various types of interactions on a global scale, whilst still maintaining high simulation speed for a large number of particles. In addition, the interplay between particle motion and interaction is well tuned in both regions of low and high densities. We analyse links between models on these two scales and prove that under certain conditions, a system of interacting particles converges to a nonlinear coupled system of transport equations. We use this as a motivation to derive a model defined on both modelling scales and prescribe the intercommunication between them. Simulation takes place in inhomogeneous domains with arbitrary conditions at inflow and outflow boundaries. We realise this by modelling obstacles, sources and sinks. Integrating these aspects into the simulation requires a route planning algorithm for the particles. Several algorithms are considered and evaluated on accuracy, robustness and efficiency. All aspects mentioned above are combined in a novel open source prototyping simulation framework called Mercurial. This computational framework allows the design of geometries and is built for high performance when large numbers of particles are involved. Mercurial supports various types of inhomogeneities and global systems of equations. We apply our framework to simulate scenarios in crowd dynamics. We compare our results with test cases from literature to assess the quality of the simulations.
nsroot: Minimalist Process Isolation Tool Implemented With Linux Namespaces
Venue: arXiv cs.DC 2016
Year: 2016

Data analyses in the life sciences are moving from tools run on a personal computer to services run on large computing platforms. This creates a need to package tools and dependencies for easy installation, configuration and deployment on distributed platforms. In addition, for secure execution there is a need for process isolation on a shared platform. Existing virtual machine and container technologies are often more complex than traditional Unix utilities, like chroot, and often require root privileges in order to set up or use. This is especially challenging on HPC systems where users typically do not have root access. We therefore present nsroot, a lightweight Linux namespaces based process isolation tool. It allows restricting the runtime environment of data analysis tools that may not have been designed with security as a top priority, in order to reduce the risk and consequences of security breaches, without requiring any special privileges. The codebase of nsroot is small, and it provides a command line interface similar to chroot. It can be used on all Linux kernels that implement user namespaces. In addition, we propose combining nsroot with the AppImage format for secure execution of packaged applications. nsroot is open sourced and available at: https://github.com/uit-no/nsroot
A Holistic Approach for Predicting Links in Coevolving Multilayer Networks
Venue: arXiv cs.SI 2016
Year: 2016

Networks extracted from social media platforms frequently include multiple types of links that dynamically change over time; these links can be used to represent dyadic interactions such as economic transactions, communications, and shared activities. Organizing this data into a dynamic multiplex network, where each layer is composed of a single edge type linking the same underlying vertices, can reveal interesting cross-layer interaction patterns. In coevolving networks, links in one layer result in an increased probability of other types of links forming between the same node pair. Hence we believe that a holistic approach in which all the layers are simultaneously considered can outperform a factored approach in which link prediction is performed separately in each layer. This paper introduces a comprehensive framework, MLP (Multilayer Link Prediction), in which link existence likelihoods for the target layer are learned from the other network layers. These likelihoods are used to reweight the output of a single layer link prediction method that uses rank aggregation to combine a set of topological metrics. Our experiments show that our reweighting procedure outperforms other methods for fusing information across network layers.